Efficient optimal Kolmogorov approximation of random variables

Cohen, Liat; Grinshpoun, Tal; Weiss, Gera

doi:10.1016/j.artint.2024.104086

Artificial Intelligence

2024

DOI: 10.1016/j.artint.2024.104086

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Efficient optimal Kolmogorov approximation of random variables

Liat Cohen,

Tal Grinshpoun,

Gera Weiss

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2024

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Hardness and Approximability of Dimension Reduction on the Probability Simplex

Bruno

2024

Algorithms

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Dimension reduction is a technique used to transform data from a high-dimensional space into a lower-dimensional space, aiming to retain as much of the original information as possible. This approach is crucial in many disciplines like engineering, biology, astronomy, and economics. In this paper, we consider the following dimensionality reduction instance: Given an n-dimensional probability distribution p and an integer m<n, we aim to find the m-dimensional probability distribution q that is the closest to p, using the Kullback–Leibler divergence as the measure of closeness. We prove that the problem is strongly NP-hard, and we present an approximation algorithm for it.

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Hardness and Approximability of Dimension Reduction on the Probability Simplex

Bruno

2024

Algorithms

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Efficient optimal Kolmogorov approximation of random variables

Cited by 1 publication

References 26 publications

Hardness and Approximability of Dimension Reduction on the Probability Simplex

Hardness and Approximability of Dimension Reduction on the Probability Simplex

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